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Conformal Prediction in Clinical Medical Sciences.

Janette Vazquez1, Julio C Facelli1

  • 1Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108 USA.

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|July 28, 2022
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Summary

Conformal predictions (CP) offer valuable insights into the accuracy of individual machine learning (ML) predictions in clinical settings. However, current research lacks clinician input and comparative analyses for effective healthcare adoption.

Keywords:
Artificial intelligence in medicineConformal Prediction, Predictive analyticsUncertainty quantification

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Artificial Intelligence in Medicine

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly utilized in medicine.
  • Assessing the accuracy of individual ML/AI predictions in clinical practice remains a challenge.
  • Conformal Predictions (CP) offer a potential framework for quantifying prediction uncertainty.

Purpose of the Study:

  • To review the existing literature on the application of Conformal Predictions (CP) in clinical settings.
  • To identify the methods and reported results of CP in clinical applications.
  • To highlight limitations and areas for future research in CP for clinical decision support.

Main Methods:

  • A comprehensive literature search was conducted using SCOPUS®.
  • Papers reporting the use of CP in clinical applications were identified and reviewed.
  • Methods and results from 14 selected studies were summarized.

Main Results:

  • Conformal Prediction (CP) methods can provide crucial insights into the reliability of individual predictions in clinical contexts.
  • Fourteen studies were identified that utilized CP for clinical applications.
  • The reviewed literature demonstrates the potential of CP to enhance the trustworthiness of ML/AI diagnostic tools.

Conclusions:

  • Conformal Predictions (CP) show promise for improving the interpretability and reliability of AI/ML models in healthcare.
  • Current research is often isolated, lacking clinical collaboration and comparative evaluations.
  • Addressing socio-technical factors and fostering interdisciplinary input are essential for the successful clinical adoption of CP.